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An improved feature extraction algorithm for robust Swin Transformer model in high-dimensional medical image
Anuj Kumar1, Satya Prakash Yadav2, Awadhesh Kumar3
1Department of Computer Science and Engineering, Abdul Kalam Technical University (AKTU), Jankipuram Vistar, Lucknow, Uttar Pradesh, 226031, India; Department of Information Technology, Management Education & Research Institute (MERI), Janak Puri, Affiliated to GGSIP University, New Delhi, India.
The Swin Transformer, a novel architecture, enhances medical image analysis by learning general features from limited data. Its adaptive attention mechanism effectively handles noisy images, achieving high accuracy in complex tasks.
Area of Science:
- Computer Vision
- Medical Image Analysis
- Deep Learning
Background:
- Medical image analysis faces challenges due to limited annotated data and image quality issues like noise and low contrast.
- High-dimensional feature extraction is crucial for complex anatomical structures in medical imaging.
Purpose of the Study:
- To propose a robust Swin Transformer architecture for high-dimensional feature extraction in medical images.
- To leverage multitask learning for improved general feature learning from limited medical data.
- To address challenges of noise, artifacts, and low contrast in medical images.
Main Methods:
- Utilized the Swin Transformer architecture, a novel deep learning model.
- Implemented an adaptive attention mechanism to dynamically adjust weights based on input quality.
- Employed an iterative transformer encoder to create a hierarchical structure for multi-scale attention.
- Trained the model using a multitask learning scheme for simultaneous analysis of various medical imaging tasks.
Main Results:
- Achieved 80.76% accuracy, 80.28% precision, 78.04% recall, and 76.46% F1-Score.
- Demonstrated effective handling of noisy and low-contrast medical images through adaptive attention.
- Showcased the model's ability to capture both local and long-range relationships in image patches at different scales.
Conclusions:
- The proposed Swin Transformer architecture offers a robust solution for high-dimensional feature extraction in medical imaging.
- The adaptive attention and hierarchical structure enable effective analysis of complex and degraded medical images.
- Multitask learning with Swin Transformer shows promise for improving generalization and performance on new medical imaging tasks.
